How usually have you ever caught your self considering, “Wouldn’t or not it’s simpler handy the venture over to AI as a substitute of paying a staff of builders?” It’s a tempting thought, particularly within the age of AI — however the actuality is way extra advanced.
On this article, we’ll discover what AI can really do in software program improvement, the place it nonetheless falls brief in comparison with people, and what conclusions corporations ought to draw earlier than entrusting a venture to synthetic intelligence.
When AI Tried to Play Software program Engineer
Just lately, a shopper approached SCAND with a singular experiment in thoughts. They wished to check whether or not synthetic intelligence might independently develop a small internet software and determined to make use of Cursor for the duty. The applying’s objective was easy — fetch statistics from an exterior API and show them in a desk.
The preliminary outcome regarded promising: AI created a functioning venture that included each client- and server-side parts, carried out the essential logic for retrieving knowledge, and even designed the interface. The desk appropriately displayed the statistics, and the general code construction appeared respectable at first look.
Nevertheless, upon nearer inspection, it grew to become clear that the answer was overengineered. As a substitute of straight connecting to the API and displaying the information within the browser, AI constructed a full backend server that proxied requests, saved intermediate knowledge, and required separate deployment.
For such a easy process, this was pointless — it difficult the infrastructure, added further setup steps, and lengthened the combination course of.
Furthermore, AI didn’t account for error dealing with, request optimization, or integration with the shopper’s present programs. This meant builders needed to step in and redo components of the answer.
The Limits of Generative AI in Coding and Software program Growth
Generative AI has already confirmed that it might probably shortly produce working code, however in apply, its capabilities in real-world software program improvement usually develop into restricted. Listed here are the important thing points we often encounter when reviewing AI‑generated tasks:
- Lack of awareness of enterprise logic and structure. AI can’t see the total image of a venture, its objectives, and its constraints. Consequently, the options it produces could also be technically right however fully misaligned with the precise enterprise wants.
- Lack of ability to make architectural commerce‑offs. An skilled software program engineer evaluates the steadiness between improvement velocity, implementation price, and ease of upkeep. AI, alternatively, can’t weigh these elements and tends to decide on an ordinary and even unnecessarily advanced strategy.
- Overengineering. Producing pointless layers, modules, and companies is a typical mistake. For instance, a easy software could find yourself with an additional backend that requires separate deployment and upkeep.
- Ignoring the context of present programs. AI doesn’t bear in mind how new code will combine with the present infrastructure, which may result in incompatibilities or further prices for rework.
- Code ≠ product. Synthetic intelligence can write fragments of code, nevertheless it doesn’t ship full options that bear in mind UX, safety, scalability, and long-term help.
- Doesn’t at all times absolutely perceive the duty. To get the specified outcome, prompts usually should be clarified or rewritten in additional element — typically stretching to a full web page. This slows down the method and forces the developer to spend time refining the request as a substitute of transferring on to efficient implementation.
Finally, regardless of the rising position of AI in software program improvement, with out the involvement of skilled builders, such tasks threat changing into a supply of technical debt and pointless prices.
Why Human Software program Builders Nonetheless Beat AI Brokers
Sure, generative AI and agentic AI can write code right this moment — typically even pretty good code. However there are nonetheless some issues that synthetic intelligence can’t exchange in knowledgeable software program developer’s workflow..
First, it’s understanding the enterprise context. A human doesn’t simply write a program — they know why and for whom it’s being created. AI sees a set of directions; a developer sees the actual process and understands the way it matches into the corporate’s objectives.
Second comes the flexibility to make knowledgeable choices — whether or not to reuse present code or construct one thing from scratch. A human weighs deadlines, prices, and dangers. AI, in flip, usually follows a template with out taking hidden prices under consideration.
Third, it’s architectural flexibility. An skilled programmer can really feel when a venture is beginning to “develop” pointless layers and is aware of when it’s the correct time to cease. AI, alternatively, usually creates extreme buildings just because that’s what it has seen in its coaching examples.
Fourth comes occupied with the product’s future. Scalability, maintainability, and dealing with edge instances are constructed right into a developer’s mindset. AI just isn’t but able to anticipating such nuances.
And eventually, communication. A real software program engineer works with the shopper, clarifies necessities, and adjusts the strategy because the venture evolves. AI just isn’t able to actual dialogue or a refined understanding of human priorities.
Due to this fact, in right this moment’s software program improvement panorama, synthetic intelligence remains to be a software — not a strategist. And within the foreseeable future, the human position in creating excessive‑high quality software program will stay important.
The desk under compares how people and AI deal with key points of improvement, and why the human position within the course of remains to be essential.
| Criterion | Software program Developer | Generative AI |
| Understanding enterprise context | Analyzes venture objectives, audience, and long-term goals | Sees solely the given immediate, with out understanding the larger image |
| Making architectural choices | Balances velocity, price, simplicity, and maintainability | Follows a template with out contemplating hidden prices |
| Structure optimization | Avoids pointless modules and simplifies when attainable | Vulnerable to overengineering, creating further layers |
| Working with present programs | Considers integration with present infrastructure | Could generate incompatible options |
| Foresight | Plans for scalability, error dealing with, and edge instances | Typically ignores non‑commonplace situations |
| Collaboration | Engages with the shopper, clarifies necessities, presents alternate options | Understands the request in a restricted manner, requires exact and detailed prompts |
| Flexibility in course of | Adapts to altering necessities on the fly | Requires code regeneration or a brand new immediate |
| Velocity of code technology | Focuses on correctness and stability over uncooked velocity | Generates code immediately, nevertheless it’s not at all times helpful or right |
| Last deliverable | Prepared‑to‑use product | A set of code requiring evaluate and refinement |
Human Builders vs AI in Software program Growth
The place AI Coding Instruments and Agentic AI Can Assist Software program Engineers
Regardless of its limitations, AI instruments have some strengths that make them priceless assistants for software program engineers. Based on Statista (2024), 81% of builders worldwide reported elevated productiveness when utilizing AI, and greater than half famous improved work effectivity.
Advantages of utilizing AI within the improvement workflow, Statista
In day‑to‑day improvement, AI can considerably velocity up routine duties and simplify supporting processes, resembling:
- Producing boilerplate code. Generative AI can produce repetitive code buildings in seconds, saving time and permitting builders to concentrate on enterprise logic.
- Creating easy parts. AI can shortly construct buttons, varieties, tables, and different UI parts that may later be tailored to the venture’s wants.
- Changing codecs. Synthetic intelligence can simply rework knowledge and code — from JSON to YAML or from TypeScript to JavaScript, and again.
- Refactoring. AI can counsel code enhancements, simplify buildings, and take away duplicates.
- Fast prototyping. AI can construct a fundamental model of performance to check concepts or reveal ideas to a shopper.
Nevertheless, even in these use instances, AI stays only a software. The ultimate model of the code ought to at all times undergo human evaluate and integration to make sure it meets architectural necessities, high quality requirements, and the venture’s enterprise context.
SCAND’s Method — AI + Human Experience within the Age of AI
At SCAND, we see synthetic intelligence not as a competitor to builders, however as a software that strengthens the staff. Our tasks are constructed on a easy precept: AI accelerates — people information.
We use Copilot, ChatGPT, Cursor, and different AI instruments the place they really add worth — for shortly creating templates, producing easy parts, and testing concepts. This permits us to avoid wasting hours and days on routine duties.
However code technology is simply the start. Each AI‑produced answer goes via the arms of our skilled builders who:
- Examine the correctness and safety of the code, together with potential license and copyright violations, since some items of the prompt code could replicate fragments from open repositories.
- Optimize the structure for the duty and venture specifics.
- Adapt technical options to the enterprise logic and venture necessities.
We additionally pay particular consideration to knowledge safety and confidentiality:
- We don’t switch confidential knowledge to public cloud-based AI with out safety, until the shopper particularly requests in any other case. In tasks involving delicate or regulated info (for instance, medical or monetary knowledge), we use native AI assistants — Ollama, LM Studio, llama.cpp, and others — deployed on the shopper’s safe servers.
- We signal clear contracts that specify: who owns the ultimate code, whether or not AI instruments are allowed, and who’s chargeable for reviewing and fixing the code if it violates licenses or comprises errors.
- We embrace obligations for documentation (AI utilization logs indicating when precisely and which instruments have been used) to trace the supply of potential points and guarantee transparency for audits.
- We offer staff coaching on AI finest practices, together with understanding the restrictions of AI-generated content material, licensing dangers, and the significance of guide validation.
Will AI Exchange Software program Engineers? The Sensible Actuality Examine
At this time, synthetic intelligence in software program improvement is on the similar degree that calculators have been in accounting a number of many years in the past: a software that accelerates calculations, however doesn’t perceive why and what numbers should be calculated.
Generative AI can already do so much — from producing parts to performing automated refactoring. However constructing a software program product is not only about writing code. It’s about understanding the viewers, designing structure, assessing dangers, integrating with present programs, and planning lengthy‑time period help for years forward. And that is the place the human issue stays irreplaceable.
As a substitute of the “AI replaces builders” situation, we’re transferring towards a blended‑staff mannequin, the place AI brokers grow to be a part of the workflow and builders use them as accelerators and assistants. This synergy is already reshaping the software program improvement panorama and can proceed to outline it within the coming years.
The principle takeaway: the age of AI doesn’t eradicate the career of software program engineer — it transforms it, including new instruments and shifting priorities from routine coding towards structure, integration, and strategic design.
Ceaselessly Requested Questions (FAQs)
Can AI write a complete app?
Sure, however usually with out optimization, with over‑engineered structure, and with out contemplating lengthy‑time period maintainability.
Will AI exchange frontend/backend builders?
Not but, since most improvement choices require enterprise context, commerce‑offs, and expertise that AI doesn’t possess.
What’s the largest affect of AI-generated code?
An elevated threat of technical debt, maintainability points, and architectural misalignment — all of which may in the end drive up the price of rework.
